The first studies to address VQG used a simple method of inputting image features into a text decoder and generating questions [1]}.
Subsequent research has focused on how to better control the content of the questions to be generated.
In general, the control of question generation is achieved by providing additional information to the text decoder in addition to the image features.
The main methods include providing the answers [2]}, [3]} and providing categories of the answers [4]}, [5]}, [6]}.
In addition, similar to this study, another study targeted knowledge that can be acquired through questioning [7]}.
They created a knowledge-aware VQG dataset (K-VQG) using AMT and used UNITER [8]}, which is a state-of-the-art vision-and-language transformer, as an encoder for images and knowledge to successfully generate questions for the knowledge.
We designed a question generation model based on their work and created a new dataset as an extension of their dataset.
